Albany 2015:Book of Abstracts
June 9-13 2015
©Adenine Press (2012)
Topological variance in protein structure: An insight from kinetics and functional study
Protein structural scaffolds facilitate the necessary geometrical and chemical space for diverse biological functions. The complex biomolecules (proteins) can be represented by several layers of modular architecture, but it is pivotal to understand and explore forces that influence the topological space. In this study, we have illustrated a simple approach to represent protein topology and investigate the structural, functional and kinetics effects of protein topologies in the observed structural space.
Using a component based approach, 3D protein structures have been converted to a 1D string ("contact string"), which incorporates sequential secondary structure and their tertiary contacts. Treating each structural class class (α, β, (α/β,) exclusively,, we have reported a set of structural patterns in each class, which are found to be prevalent in protein structure space.
Our results indicate statistically significant presence of observed structural patterns as modules or "building blocks" in large proteins (higher secondary structure content). From structural descriptor analysis, observed patterns are found to be within similar deviation, however frequent patterns are found to be distinctly occurring in diverse functions e.g. in enzymatic classes and reactions (Khan & Ghosh, 2014). We have further analyzed and compared the kinetic accessibility of structural patterns using structure based models (SBM) and Go-like potential (Lammert, Schug, & Onuchic, 2009). Monitoring the folding/unfolding transitions, our result shows that homogeneity in tertiary contacts contributes to the distinct transition phases (unfolding/folding basins). The balance between tertiary and local contacts contributes to the cooperative nature of folding, which are found to be prominent characteristic in prevalent structural patterns.
Lammert, H., Schug, A., & Onuchic, J. N. (2009). Robustness and generalization of structure-based models for protein folding and function. Proteins, 77(4), 881-91.
School of Computational & Integrative Sciences